Arteriovenous retinal vessel optic disk positioning method of eye fundus image

Abstract

The invention discloses an arteriovenous retinal vessel optic disk positioning method of an eye fundus image. The method comprises the following steps: acquiring a global vessel set of the eye fundus image, wherein the global vessel set is a set of all vessels in the eye fundus image; acquiring a convergence region of each vessel by using a fuzzy convergence algorithm aiming at each vessel in the global vessel set; counting the number of the convergence regions to which each pixel point of the eye fundus image belongs to serve as voting value of the pixel point; selecting the previous n pixel points with large voting values, applying an eight connection-based region communication algorithm to the selected n pixel points to obtain a plurality of communication regions, and taking a central coordinate of the communication region with the maximum area as the optic disk positioning information. According to the optic disk positioning method, a multiple positioning voting mode is adopted, the method has robustness and behaves well under the complex condition of more diseases in the eye fundus image, finally a template is added for matching to serve as positioning and non-convergent correction, and the rejection is less compared with the method that the condition is not treated.

Description

technical field [0001] The invention relates to the technical field of computer-aided diagnosis, in particular to a method for locating arteriovenous, retinal, and optic discs of fundus images. Background technique [0002] With the rapid development of the field of artificial intelligence in computer technology, computer-aided diagnosis technology is also gradually developed. Computer-aided diagnosis technology refers to the use of imaging, medical image processing technology and other possible physiological and biochemical means, combined with computer analysis and calculation, to assist radiologists in finding lesions and improving the accuracy of diagnosis. [0003] Usually, computer-aided diagnosis in medical imaging is divided into three steps, as follows: the first step is to extract the lesion from the normal structure; the second step is to quantify the image features; the third step is to process the data and draw conclusions . [0004] Because the computer can f...

AI Technical Summary

Problems solved by technology

[0007] Blood vessel segmentation, optic disc positioning and vessel classification (arteriovenous split) on fundus images are the basis of retinal vessel lesion detection. The existing blood vessel segmentation methods need to manually add labeling information, and the degree of automation is not high.

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